Prediction of High-Grade Pediatric Meningiomas: Magnetic Resonance Imaging Features Based on T1-Weighted, T2-Weighted, and Contrast-Enhanced T1-Weighted Images

A Novel MRI-Based Risk Stratification Algorithm for Predicting Postoperative Recurrence of Meningioma: More Benefits to Patients

Pathological grading of meningioma is insufficient to predict recurrence after resection and to guide individualized treatment strategies. One hundred and thirty-three patients with meningiomas who underwent total resection were enrolled in this retrospective study. Univariate analyses were conducted to evaluate the association between factors and recurrence. Least absolute shrinkage and selection operator (Lasso) was used to further select variables to build a logistic model. The predictive efficiency of the model and WHO grade was compared by using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and net reclassification improvement (NRI). Patients were given a new risk layer based on a nomogram. The recurrence of meningioma in different groups was observed through the Kaplan-Meier curve. Univariate analysis demonstrated that 11 risk factors were associated with prognosis (P < 0.05). The result of ROC proved that the quantified risk-scoring system (AUC = 0.853) had a higher benefit than pathological grade (AUC = 0.689, P = 0.011). The incidence of recurrence of the high risk cohort (69%) was significantly higher than that of the low risk cohort (9%) by Kaplan-Meier analysis (P < 0.001). And all patients who did not relapse in the high risk group received adjuvant radiotherapy. The novel risk stratification algorithm has a significant value for the recurrence of meningioma and can help in optimizing the individualized design of clinical therapy.

Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas

PURPOSE

Meningiomas are mainly benign tumors, though a considerable proportion shows aggressive behaviors histologically consistent with atypia/anaplasia. Histopathological grading is usually assessed through invasive procedures, which is not always feasible due to the inaccessibility of the lesion or to treatment contraindications. Therefore, we propose a multi-parametric MRI assessment as a predictor of meningioma histopathological grading.

METHODS

Seventy-three patients with 74 histologically proven and previously treated meningiomas were retrospectively enrolled (42 WHO I, 24 WHO II, 8 WHO III) and studied with MRI including T2 TSE, FLAIR, Gradient Echo, DWI, and pre- and post-contrast T1 sequences. Lesion masks were segmented on post-contrast T1 sequences and rigidly registered to ADC maps to extract quantitative parameters from conventional DWI and intravoxel incoherent motion model assessing tumor perfusion. Two expert neuroradiologists assessed morphological features of meningiomas with semi-quantitative scores.

RESULTS

Univariate analysis showed different distributions (p < 0.05) of quantitative diffusion parameters (Wilcoxon rank-sum test) and morphological features (Pearson's chi-square; Fisher's exact test) among meningiomas grouped in low-grade (WHO I) and higher grade forms (WHO II/III); the only exception consisted of the tumor-brain interface. A multivariate logistic regression, combining all parameters showing statistical significance in the univariate analysis, allowed discrimination between the groups of meningiomas with high sensitivity (0.968) and specificity (0.925). Heterogeneous contrast enhancement and low ADC were the best independent predictors of atypia and anaplasia.

CONCLUSION

Our multi-parametric MRI assessment showed high sensitivity and specificity in predicting histological grading of meningiomas. Such an assessment may be clinically useful in characterizing lesions without histological diagnosis. Key points • When surgery and biopsy are not feasible, parameters obtained from both conventional and diffusion-weighted MRI can predict atypia and anaplasia in meningiomas with high sensitivity and specificity. • Low ADC values and heterogeneous contrast enhancement are the best predictors of higher grade meningioma.

Current decision-making in meningiomas

The incidence of meningiomas is rising and the number of incidental cases is increasing steadily. The efficiency and the safety of each treatment strategy are also improving over time. Therefore the indications to treat meningiomas are constantly changing. The aim of meningioma treatment is keeping the patient fully functional while achieving long-term relief or prevention from problems related to intracranial tumor growth. This chapter reviews the natural history and treatment results and aims to put together the information for the most objective decision-making in treating meningiomas. Factors acting on the treatment decision such as anatomical localization, symptomatology, variations in tumor biology, recurrence status, age and co-morbidities, operative gains, and patient preference are individually discussed.

Clinical, radiological, and histopathological predictors for long-term prognosis after surgery for atypical meningiomas

BACKGROUND

Despite considerable rates of recurrence and mortality in atypical meningiomas, reliable predictors for estimating postoperative long-term prognosis remain elusive.

METHODS

Clinical, histopathological, and radiological variables from 138 patients, including 64 females and 74 males (46% and 54%, median age 62 years), who underwent surgery for intracranial atypical meningioma were retrospectively analyzed. Associations between variables and recurrence and mortality were investigated using uni- and multivariate analyses.

RESULTS

Gross total (GTR) and subtotal resection (STR) was achieved in 81% and 19% of cases, respectively. Within a median follow-up of 62 months, recurrence occurred in 52 (38%) and mortality in 22 (16%) cases. In patients who did not receive adjuvant irradiation, recurrence rates were higher after STR than after GTR (32% vs 63%, p = 0.025). In univariate analyses, only intratumoral calcifications on preoperative MRI (p = 0.012) and the presence of brain invasion in the absence of other histological grading criteria (p = 0.010) were correlated with longer progression-free intervals (PFI). In multivariate analyses, patient age was positively (HR 1.03, 95%CI 1.04-1.05; p = 0.018) and the presence of brain invasion as the only grading criterion (HR 0.37, 95%CI 0.19-0.74; p = 0.005) was negatively related with progression, while rising age at the time of surgery (HR 1.07, 95%CI 1.03-1.12; p = 0.001) was prognostic for mortality.

CONCLUSIONS

PFI was longer in brain invasive but otherwise histological benign meningiomas and in tumors displaying calcifications on preoperative MRI. Advancing patient age and lower Karnofsky Performance Score were associated with higher overall mortality.

Prediction of High-Grade Histology and Recurrence in Meningiomas Using Routine Preoperative Magnetic Resonance Imaging: A Systematic Review

OBJECTIVE

Estimating the risk of recurrence after surgery remains crucial during care of patients with meningioma. Numerous studies identified correlations of characteristics on routine preoperative magnetic resonance imaging (MRI) with postoperative recurrence or high-grade histology but showed partially inconclusive results.

METHODS

A systematic review of the literature was performed about findings on preoperative MRI and their correlation with high-grade histology and recurrence. Quality of the included studies was analyzed using standardized Quality Assessment of Diagnostic Accuracy Studies criteria.

RESULTS

Among the 35 studies included, quality of the series according to the Quality Assessment of Diagnostic Accuracy Studies criteria differed widely. Remarkably, MRI variables found to be associated with high-grade histology were commonly not consistently associated with prognosis and vice versa. Correlations of the tumor size, the peritumoral edema size, and contrast-enhancement of the tumor capsule with high-grade histology were controversial. In most studies, non-skull base tumor location, cyst formation, heterogenous contrast-enhancement, an irregular tumor shape, and disruption of the tumor/brain border but not intensity of the lesion on T2-weighted images, calcifications, or bone involvement were associated with grade II/III histology. Although tumor and edema size were usually found to correlate with recurrence, heterogenous contrast enhancement, cyst formation, intensity of the tumor on T2-weighted MRI, and enhancement of the tumor capsule were mostly not related with progression.

CONCLUSIONS

Several mostly consistent but partially inconsistent variables associated with high-grade histology or prognosis were identified. Although standardized studies are needed to provide further clarification, consideration of these findings can help to improve estimation of prognosis and can therefore improve postoperative care in patients with meningioma.

Prediction of brain invasion in patients with meningiomas using preoperative magnetic resonance imaging

Brain invasion (BI) in meningiomas impacts WHO grading and therefore adjuvant treatment. However, BI is rare and neurosurgical sampling and neuropathological analyses are not standardised. Moreover, associations with imaging findings are sparsely known. Associations between BI and findings on preoperative MRI were investigated in 617 meningioma patients. BI was strongly correlated with other high-grade criteria (p<.001). Presence of a contrast enhancing tumour capsule, disruption of the arachnoid layer, intratumoural calcifications and T2-intensity were not related to high-grade histology or BI (p>.05, each). High-grade histology (p=.033) but not BI (p=.354) was associated with tumour location. Irregular tumour shape (OR: 3.33, 95%CI 1.33-8.30; p=.007), heterogeneous contrast enhancement (OR: 2.82, 95%CI 1.19-6.70; p=.015) and peritumoural edema (OR: 1.005 per ccm, 95%CI 1.001-1.008); p=.011) were associated with BI. Multivariable analyses identified only increasing edema volume (OR: 1.005 per ccm, 95%CI 1.002-1.009; p=.010) as a predictor for BI, independent of other histopathological high-grade criteria. We finally provide a new model to estimate the risk of BI using routine preoperative MRI. Several imaging characteristics were identified as predictors for BI. Consideration in clinical routine can increase the accuracy of the detection in neuropathological analyses.